Volume 15, Issue 4

This paper presents the current state of the art of noninvasive glucose monitoring. In recent years, we can observe constant increase in the incidence of diabetes. About 40% of all performed blood tests apply to the glucose tests. Formerly, this lifestyle disease occurred mainly in rich countries, but now it is becoming more common in poorer countries. It is related to the increase in life expectancy, unhealthy diet, lack of exercise, and other factors. Untreated diabetes may cause many complications or even death. For this reason, daily control of glucose levels in people with this disorder is very important. Measurements with a traditional glucometer are connected with performing finger punctures several times a day, which is painful and uncomfortable for patients. Therefore, researches on other methods are ongoing. A method that would be fast, noninvasive and cheap could also enable testing the state of the entire population, which is necessary because of the number of people currently living with undiagnosed type 2 diabetes. Although the first glucometer was made in 1966, the first studies on glucose level measurement in tear film were documented as early as 1937. This shows how much a noninvasive method of diabetes control is needed. Since then, there have been more and more studies on alternative methods of glucose measurement, not only from tear fluid, but also from saliva, sweat, or transdermally.

Along with the development of modern science, medical knowledge and therapy become more and more precise and personal as a consequence. Genetics and immunology participate in the progress in particular. They open the way to molecular knowledge, allowing precise interpretation of pathology in individual cases followed by finding proper therapy. However, the large-scale improvement of medical efficacy seems to be achieved with the development of screening tests that, being not invasive and cheap, may allow for personal repeatable use and early revealing of threatening diseases.

Wireless body sensor networks (WBSNs) play a vital role in monitoring the health conditions of patients and are a low-cost solution for dealing with several healthcare applications. However, processing a large amount of data and making feasible decisions in emergency cases are the major challenges attributed to WBSNs. Thus, this paper addresses these challenges by designing a deep learning approach for health risk assessment by proposing fractional cat based salp swarm algorithm (FCSSA). At first, the WBSN nodes are utilized for sensing data from patient health records to acquire certain parameters for making the assessment. Based on the obtained parameters, WBSN nodes transmit the data to the target node. Here, the hybrid harmony search algorithm and particle swarm optimization (hybrid HSA-PSO) is used for determining the optimal cluster head. Then, the results produced by the hybrid HSA-PSO are given to the target node, in which the deep belief network (DBN) is used for classifying the health records for the health risk assessment. Here, the DBN is trained using the proposed FCSSA, which is developed by integrating fractional cat swarm optimization (FCSO) and salp swarm algorithm (SSA) for initiating the classification. The proposed FCSSA-based DBN shows better performance using metrics, namely accuracy, energy, and throughput with values 94.604, 0.145, and 0.058, respectively.

Disability, specifically impaired upper and/or lower limbs, has a direct impact on the patients’ quality of life. Nowadays, motorized wheelchairs supported by a mobility-aided technique have been devised to improve the quality of life of these patients by increasing their independence. This study aims to present a platform to control a motorized wheelchair based on face tilting. A real-time tracking system of face tilting using a webcam and a microcontroller circuit has been designed and implemented. The designed system is dedicated to control the movement directions of the motorized wheelchair. Four commands were adequate to perform the required movements for the motorized wheelchair (forward, right, and left, as well as stopping status). The platform showed an excellent performance regarding controlling the motorized wheelchair using face tilting, and the position of the eyes was shown as the most useful face feature to track face tilting.

Diarrhea, defined as passing watery stool for three times or more per 24 h, may affect children’s growth and development and possesses a high mortality rate, particularly among children under 5 years old. Those poor outcomes result from water and electrolyte imbalance and poor nutrition status in children suffering from diarrhea. Management of childhood diarrhea, aiming to prevent dehydration and malnutrition, encompasses those covered in LINTAS DIARE (“Lima Langkah Tuntaskan Diare”) – guidance from the Ministry of Health of Indonesia to overcome childhood diarrhea in five steps. A concept of an Android-based application is designed in both English and Bahasa versions to guide parents as users in a home-based approach to recognize the dehydration status and danger signs of children with diarrhea. The application may also allow users to monitor water balance as well as management adequacy during the diarrhea period. Data such as child’s birth date, weight, and height; stool’s characteristics and frequency; and dehydration and warning signs are required to be input by users to allow the program to classify the dehydration status and suggest recommendation of actions. The advice provided is based on the LINTAS DIARE principles.

Three-dimensional protein structure prediction is an important task in science at the intersection of biology, chemistry, and informatics, and it is crucial for determining the protein function. In the two-stage protein folding model, based on an early- and late-stage intermediates, we propose to use state-of-the-art secondary structure prediction servers for backbone dihedral angles prediction and devise an early-stage structure. Early-stage structures are used as a starting point for protein folding simulations, and any errors in this stage affect the final predictions. We have shown that modern secondary structure prediction servers could increase the accuracy of early-stage predictions compared to previously reported models.